cwx-worst-one/EAT
[IJCAI 2024] EAT: Self-Supervised Pre-Training with Efficient Audio Transformer
This project helps researchers and developers working with audio data to analyze and classify sounds more efficiently. It takes raw audio files as input and extracts high-level features or provides classifications of the sounds, helping to identify what is happening in an audio clip. Anyone building applications that interpret sounds, such as environmental monitoring, sound event detection, or speech processing, would find this useful.
221 stars.
Use this if you need a powerful and efficient way to extract meaningful features from raw audio or classify sounds for various audio-related tasks.
Not ideal if you are looking for a simple, out-of-the-box solution for basic audio playback or editing, as this tool is focused on deep learning for audio analysis.
Stars
221
Forks
14
Language
Python
License
MIT
Category
Last pushed
Nov 30, 2025
Commits (30d)
0
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